Software Cost Research Articles

Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications

The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.

Optimization of Closed-loop Supply Chain Network for Vegetable and Fruit Agricultural Products Considering Fuzzy Demand and Purchasing Price

Due to the seasonal and perishable nature of vegetable and fruit agricultural products, which can easily cause sudden changes in demand and prices in a short period of time, a decision model framework was established to address the uncertainty issues related to vegetable and fruit agricultural products. The framework consists of triangular fuzzy numbers, a closed-loop supply chain network model for vegetable and fruit agricultural products, and an optimization model based on chance constrained programming, while considering demand, return rate,utilization rate. And with the goal of minimizing the sum of fixed costs, transportation costs, purchasing costs, packaging costs, storage costs, processing cost and disposal costs in the supply chain network, the model is validated through two scenarios: fuzzy demand and deterministic demand. The model calculation results indicate that under certain other conditions, the return rate is positively correlated with the total cost; The relationship between utilization rate and total cost may be either positive or negative, influenced by the combined effects of transportation costs and disposal costs in reverse logistics activities; The cost increase and facility selection corresponding to adjacent demand proportion nodes are different. Proved the effectiveness and feasibility of the model.

Teaching Information and Communication Technology (ICT) in Zimbabwean Polytechnical Colleges: Success and Challenges

The aim was to investigate the success and challenges of Information Communication Technology (ICT) in Zimbabwean Polytechnics. The study was guided by the objectives that included assessing the success in common computing skills levels among the staff and students, investigating levels of usage of ICT skills adoption and integration between students and staff, and to investigate the challenges in ICT skills adoption and integration between staff and students. Relevant literature was covered in chapter two. The literature was presented by referring to different researchers that wrote on challenges of ICT adoption and integration in the form of constructive argument by the aid of authors in support and those against. The research adopted interprevist research philosophy he target population of the study constituted 220 employees of the polytechnics and was reduced to 140 making use of sample size determination model by Krejice and Morgan. Operating systems, word processing, spreadsheets, presentation software, and use of the internet were the common computing skills level among the staff and students. Frequency use of ICT tools in teaching and learning, frequency use of computers and the Internet for teaching and learning purposes, mandatory use of computers for teaching and learning, students being conversant with the use of ICT tools, and having a college budget to support ICT skills development determined the levels of usage of ICT skills adoption and integration between students and staff. The challenges in ICT skills adoption and integration between staff and students were lack of funding from the government and stakeholders, improper ICT infrastructure, expensive hardware and software costs and lack of skills and expert knowledge. The recommendations of the study were that the institutions should upgrade their ICT infrastructure and resources in order to maintain the success in computing skills level of staff and students, come up with short computer courses with diverse computer packages to enhance the level of ICT skills usage of staff and students and identify critical needs in the community and come up with potential projects to sustain themselves and build a reliable ICT infrastructure.

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs.

Data-driven agile software cost estimation models for DHS and DoD

Problem:Since the Manifesto for Agile Software Development, the cost analysis community has struggled to find the most appropriate size measures for developing accurate agile software development cost estimates at an early phase to establish baseline budgets and schedules, and for the selection of competitive bidders. At this time, common agile sizing measures such as Story Points are not practical since these are reported months after contract award in the Department of Homeland Security (DHS) and Department of Defense (DoD). The problem is compounded with the lack of data to build estimation models for informed decisions. Aims:The primary objective is to investigate how well two new size measures (Functional Story and Issues) accurately relate to total effort, and how these compare and rank against four popular software size measures (Story, Story Point, Unadjusted Function Points, and Simple Function Points). The second objective is to rank the six sizing measures based on how well each estimate software development effort at an early phase or after contract award. Method:The experimental framework relied on an analysis of variance and goodness-of-fit tests to examine and compare the accuracy of effort estimation models using six competing size measures. The analysis is based on data from 17 agile projects implemented between 2014 to 2021. Results:Our two new size measures (Functional Story, Issues) proved to be good predicters of total software development effort. Functional Story is better at predicting total effort at early phase than Function Points and Story. Functional Story and Function Points are better at predicting total effort after contract award than Story Point and Issues. Conclusion:The DHS and DoD cost community can choose one or more of these estimation models to evaluate agile software development cost proposals or track agile developer’s progress after contract award.

Certain aspects of efficiency of crop production digitalization in the Republic of Belarus

Digitalization, or digital transformation, is an innovative approach to solving the key task of agricultural production in the Republic of Belarus – increasing / maintaining the profitability of crop production. Digitalization makes it possible to bring crop production to a new level of development, providing a fundamental restructuring of the decision-making and implementation of agro-technological operations, reducing risks and expanding possibilities of sustainable and efficient crop production. Efficiency of digitalization of crop production is considered in relation to certain aspects of an average agricultural organization in the Republic of Belarus. The following main interrelated areas of digitalization in crop production have been identified: soil-and-agrochemical analysis, precision arable farming, prediction of the diseases and pests of crops. Due to significant growth of energy prices nowadays (basic elements in the formation of the final cost of crop production), efficiency of digitalization was assessed not directly in terms of value, but in reducing the costs of growing products and in saved crops. The greatest effect, according to the results of the assessment, is expected from such areas of digitalization as soiland-agrochemical analysis, as well as prediction of the diseases and pests of agricultural crops. The results of soil-and-agrochemical analysis can be used to improve the soil fertility – a vital condition for long-term increase of crop yields. Prediction of the diseases and pests of crops is one of the main elements of comprehensive protection of crops – a large reserve for increasing crop production. At the same time, despite the positive aspect of digitalization in agricultural branch of the Republic of Belarus, it is rather a labor-intensive process together with the high cost of necessary equipment and software, as well as the use complexity.

Troubleshooting the Poor Flow Problem of Valsartan Drug Powder Using the Crystallo-Co-Agglomeration Technique.

Pharmaceutical tablets are a popular solid dosage form and have a significant ratio in the available solid dosage forms. They are a popular choice for patients due to the ease of administration as well as for pharmaceutical manufacturers due tothe low cost of manufacturing, packaging, and other pharmaceutical parameters. However, the drug powder should either be crystalline or turned into a granular form using wet-dry granulation techniques to improve the flow and compressibility. The valsartan drug, which is commonly used as an antihypertensive drug, is an amorphous drug and has an angle of repose of more than 40º. Therefore, it needs to be converted into a granular form. This work uses the spherical crystals of the valsartan drug because they flow well and can be used for pharmaceutical tablets. Different process parameters, such as mixing speed, mixing time, and temperature, were optimized to obtain effective process parameters. The final batch of spherical crystals of valsartan had an angle of repose of 27.23º, which shows that prepared spherical crystals flow well.

Towards optimal virtual machine placement methods in cloud environments

The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends.

Virtual Reality in Vocational Education and Training: Challenges and Possibilities

This article explores the challenges and possibilities in use of virtual reality (VR) as a pedagogical tool in vocational education at school level. VR has the potential to transform vocational education by providing immersive, interactive, and realistic simulations of real-world work environments to the students. The article discusses the advantages of using VR in vocational education, including increased engagement, motivation, and knowledge retention of the students. It also provides the case studies on successful integration of VR in vocational education and training. It also examines the challenges of implementing VR in vocational education, such as the cost of equipment and software, and the need for specialized technical support. The article concludes with a discussion of future directions for research on the use of VR in vocational education and the potential impact on the future of work.

REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT

Mass customization is related to increasing the balance between the needs of companies that are focused on customers on conditions of production flexibility and efficiency. Product adjustment according to customer needs can increase the company's competitiveness. However, special production processes and adjustments are time consuming and cost inefficient. Parametric product modeling is a fairly popular technique for dealing with this problem. However, it still has challenges related to the high cost of software and a workforce that has special expertise in the field of quality control. In addition, product-specific designs cannot be tested quickly, resulting in a long production time. This study proposes a machine learning (ML) method that aims to obtain a fast time structure to analyze the production of orthopedic fixators. This research process requires a collection of training data with product attributes, physical characteristics, quality, selected ML techniques, and determination of the appropriate set of hyperparameters. Optimization results were obtained using the gradient boosting method with a value of . With these results, the orthopedic fixation device can be used in the case study of developing this machine learning model.

Life-cycle economic and environmental impacts of municipal solid waste reverse logistics in residential areas

The economic and environmental impacts of the reverse logistics (RL) process (including drop-off, collection and transportation [C&T]) of the waste disposal chain are becoming increasingly prominent with the increasing generation of municipal solid waste (MSW) and promotion of MSW classification. Quantitative evaluation of this process from economic and environmental perspectives is of great significance for MSW management. This study focused on the financial capital, materials, and energy consumption in the RL process in Xi an City, China. Based on field investigation, the magnitude of pollutant emissions from MSW C&T vehicles over their life cycle was predicted using the GREET software and total RL life-cycle cost and life-cycle assessment were analyzed. The results showed that the finical costs of RL were $46.35–$49.03 per ton of food waste and $62.52–$88.84 per ton of residual waste; the environmental impacts caused by the RL process accounted for 79.24%–96.00% and 20.87%–68.55% of the entire food waste and residual waste management chains, respectively. Labor costs were the biggest financial expenditure and the fuel cycle of C&T vehicles caused the majority of the environmental impacts. Source-separated waste management scenarios represented more environmental benefits but poorer economic positions. In the future, improving MSW source-separation accuracy, replacing diesel C&T vehicles with electric ones, and optimizing the RL system could reduce the environmental and economic impacts of the entire waste management system.

SOA for Dynamically Integrated Virtual Learning Environment Systems with Cloud Based Services

SOA is structural approach for creating services to be reused and shared, so it provides agility and cost saving in software development by dividing the application into multiple software components to be reused in other systems. Cloud computing is truly scalable and provide virtualized resources which users can subscribe. Using a cloud and SOA in virtual learning systems provide a great chance for learners to enhance gained learning outcomes. The adoption of cloud services also assists in reducing the cost of software, hardware, human resources and infrastructure. This paper will use SOA and cloud computing to transfer virtual learning systems in the cloud to be more integrated and interoperable through showing a conceptual model of distributed virtual learning system and using cloud computing combined with services oriented architecture, to contribute in interoperability and integration of e-learning systems in general

Incorporation of Plants Extracts and Essential Oils into Starch‐Based Films: A Comprehensive Review

AbstractGiven the global environmental issues associated with petroleum‐based packaging materials, developing green packaging films is a promising innovative approach to packaging food products. In this regard, starch films added with plant extracts and essential oils have been explored for different packaging types of food products. The availability of starch in nature makes starch films a cheap substitute for petroleum‐based packaging material, thus reducing the impact on the environment, packaging costs, and complexity. Recent investigations emphasize identifying and isolating natural ingredients from different plants for use in starch‐based green packaging. Plant extracts and essential oils influence the mechanical and barrier properties and impart antioxidant and antimicrobial properties to the developed films. These films show promising applications in the food industry. This review provides recent insights into the effects of plant extracts and essential oils on starch‐based green packaging films' properties.

560 Mechanical Thrombectomy Versus Best Medical Therapy for Medium Vessel Occlusions: A Cost-Effective Analysis Using Local Get with the Guidelines Registry Data

INTRODUCTION: Equipoise exists in regards to whether mechanical thrombectomy (MT) is superior to best medical management (BMM) in patients with acute ischemic stroke due to medium vessel occlusion (MeVO). While clinical outcome differences may not be statistically significant in prior series, MT for MeVO may still be cost-effective (CE). METHODS: The local Get with the Guidelines (GTWG) registry data was queued. Patients with MeVO documented on CTA presenting with NIHSS >5 and treated with MT or BMM within 12 hours from symptom onset were included. Rates of each mRS outcomes was collected. Stroke cost data was derived from previous stroke literature including costs associated with imaging done at admission (non-contrast CT, CT Angiography, and CT perfusion), MRI 24 hours post-stroke, perfusion maps software, intravenous alteplase, physician and personnel costs, and thrombectomy. Costs associated with each mRS from presentation to 90-days and post-90 day care were collected. Utilizing the clinical data from GTWG registry and costs from the literature, TreeAge Pro was used to generate cost-effectiveness ratios (CE-ratio). RESULTS: Both the MT and BMM groups were similar in age, comorbidities, NIHSS and time to presentation. MT was found to be more cost-effective (CE-ratio 93,500) compared to BMM (CE-ratio 101,496. MT was more costly (total cumulative cost $59,840) than BMM (total cumulative cost $51,763). MT and BMM respectively had an effectiveness score of 0.64 and 0.51. CONCLUSIONS: This is one of the first CE studies comparing MT and BMM in acute ischemic strokes due to MeVO. We demonstrate based on GTWG data and costs in the literature that MT is more CE than BMM.

Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism

Accurately estimating the cost of software development is crucial for effective project planning and resource allocation. However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors. This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy. The proposed approach estimates project cost based on the effort required to complete software development, which is a key driver of the project cost. The proposed model is designed to address the challenges posed by the variability in open-source development, including variable team sizes, working hours, and expertise. The study collects and pre-processes data from open-source platforms and selects cost drivers and metrics based on logical rules and SQL queries. Moreover, we propose an optimized Artificial Neural Network (ANN) with augmented topology to automate the selection of neuron units, layers, and adjustment of learnable parameters according to the input variables. The proposed model is evaluated on a 100 open-source software repositories dataset and demonstrates its effectiveness in accurately estimating development cost. The system is implemented using Python and evaluated using performance parameters such as MSE, RMSE, MAE, and MMRE. Results indicate that our proposed model offers a more accurate and efficient approach to software cost estimation, especially for freelancers and outsourcing firms. The proposed model has the potential to save time and resources and improve the reliability and accuracy of software cost estimation.

PHOTOGRAMMETRIC METHOD OF OBTAINING A DIAGNOSTIC 3D MODEL OF THE JAW

Solving the problem of obtaining a highly precise three-dimensional digital model of the jaws is of extreme importance, given the widespread introduction of computer diagnostics and precision manufacturing methods into dental practice. The high cost of scanning equipment and software limits the possibility of using digital protocols by a wide range of doctors, therefore, photogrammetry is one of promising areas of scientific research and improvement of the existing developments on the way to solving this problem. Simple, generally available and relatively cheap equipment for photogrammetry allows rapid implementation of digital protocols into dental practice. Having studied the literature sources on the technical implementation of the photogrammetric method of obtaining a three-dimensional model of the jaws, the authors suggested their own algorithm of photographing the physical model and further processing of digital information to obtain a 3D model. The algorithm consisted of 50 frame photographing of a plaster model. Photography was carried out in two series of 25 pictures each (horizontally and at 45 degrees angle to the occlusal plane of the model dentition. The technical difference of the suggested method is the use of a rotary table with colored marker tapes and the maximum LED lighting of the photobox, which ensures shadowless photography. The digital photogrammetric processing of the received photos was carried out with the help of Zephyr Free v.6.513 software (3DFlow, Italy). This free software allows you to obtain and archive a 3D model of the jaw for further diagnostic or technical work. The high visual quality of the virtual models obtained by the abovementioned method requires further experimental verification in terms of exact correspondence to the original.

Determinants of Customer Switching Behaviour in Mobile Telecommunication Industry in South-South Nigeria

The study examined the determinants of customers’ switching behaviour in mobile telecommunications industry in South-South Nigeria. The main objective of the study was to explore the factors influencing customers’ switching behaviour in the mobile telecommunications industry and in specific terms, determine whether price, reputation, service quality, promotional package and switching cost did have a significant and positive influence on customers’ switching behaviour. The unit of analysis comprised 400 mobile telecommunication subscribers drawn from six cities in the South-South geopolitical zone of Nigeria. The study units were selected using a proportionate stratified sampling technique and the data obtained from the process were analysed with descriptive statistic using Statistical Package for Social Sciences (SPSS) version 23 software. Conceptual hypothesized relationships involving the independent and dependent variables formulated from the research model were tested with multiple regression analysis technique. The findings of the study revealed that reputation, service quality, promotional package and switching cost had a strong and positive relationship with customers’ switching behaviour but price and involuntary switching seemed to have the least significant relationship. It showed that those factors were very important determinants of customers’ decision to switch from one mobile telecommunications provider to another except price and involuntary switching as earlier stated. The key recommendations from the study were that managers of mobile telecommunications companies should develop proper pricing strategies that were remarkably different in terms of moderate call rates and data charges to prevent customer switching. Secondly, plans could be put in place by management that would encourage the development of trust and confidence through the provision of timely quality services and ensure good network coverage. Finally, managers should ensure that promotional campaigns were aimed at building relationships. In conclusion, it is quite important that managers should have adequate knowledge of the determinants of customers’ switching behaviour established in the study so as to put in place preventive measures that will enhance customers’ retention of improved sales and profitability. The study made a significant contribution by identifying customers’ perceptions of marketing variables that could affect switching.

Drivers and barriers to digitalisation: a cross-analysis of the views of designers and builders in the construction industry

The construction industry around the world has identified the significance of digitalisation, and a wide range of research has explored the drivers and barriers of digitalisation within the construction industry. However, none has compared the views of designers and builders separately. Hence, this research critically reviewed drivers and barriers that affect the digitalisation of construction for designers and builders with an in-depth cross analysis. A detailed survey was conducted to capture drivers and barriers to digitalisation impacting the building sector in NSW, Australia. The survey captured the views of 542 respondents (347 designers and 195 builders), enabling the interpretation of results at a 95% level of confidence. Descriptive statistics were interpreted and cross analysis between designers and builders, and within different organisation sizes were conducted using the Mann-Whitney U test and Kruskal-Wallis H test. The survey findings were further verified through a series of primary stakeholder interviews (designers, builders, and software service providers). The findings of the survey, the cross-analysis, and the interviews were triangulated and collectively discussed to derive an in-depth understanding of drivers and barriers. ‘Greater level of accuracy and trustworthiness’ was rated the most important driver by both designers and builders, despite having significantly different response profiles. On the other hand, ‘high cost of software’ was rated the greatest barrier to digitalisation by both parties, whereas their response profiles were similar according to the cross analysis. In contrast to the designers, all builders had rated the top barriers in a similar way, irrespective of the size of the organisations. The results provide a greater understanding on the diverse views of designers and builders on drivers and barriers to the digitalisation of construction in NSW. This will direct government programmes and policy decisions to avoid considering both designers and builders as a monolithic block, when aiming at improving the state of digitalisation and performance of the construction industry.

Multiplicative Attacks with Essential Stealthiness in Sensor and Actuator Loops against Cyber-Physical Systems.

Stealthy attacks in sensor and actuator loops are the research priorities in the security of cyber-physical systems. Existing attacks define the stealthiness conditions against the Chi-square or Kullback-Leibler divergence detectors and parameterize the attack model based on additive signals. Such conditions ignore the potential anomalies of the vulnerable outputs in the control layer, and the attack sequences need to be generated online, increasing the hardware and software costs. This paper investigates a type of multiplicative attack with essential stealthiness where the employed model is a novel form. The advantage is that the parameters can be designed in a constant form without having to be generated online. An essential stealthiness condition is proposed for the first time and complements the existing ones. Two sufficient conditions for the existence of constant attack matrices are given in the form of theorems, where two methods for decoupling the unknown variables are particularly considered. A quadruple-tank process, an experimental platform for attack and defense, is developed to verify the theoretical results. The experiments indicate that the proposed attack strategy can fulfill both the attack performance and stealthiness conditions.

A metadata driven process for assessing stability and reusability based on risk of change of software systems

AbstractMeasuring and estimating the reusability of software components are important steps toward finding reusable candidates. Reuse of software components can aid in the reduction of the development cost of new software or maintenance of existing ones. However, assessing the reusability of software is a challenging task. Even if reusable candidates can be identified, developers will need to decide on the reuse strategies, that is, reuse as it is without any modification, or introduce new changes to the reusable candidate to fulfill requirements in the new environment or releases. Thus, the stability of the reusable candidates plays a pivotal role because it will influence the ease of reuse. Assessing and predicting the stability of potential reusable candidates becomes a significant and important step in software reuse. In this research, we propose to leverage on risk of change as a proxy to measure software stability, where the latter is also a proxy measure for software reuse. Based on experiments conducted on 29 open‐source software with different sizes and application types hosted on GitHub, we found that classes with high impact and high risk of change should be avoided from being reused due to their instability, while those classes with low impact and risk of change should be given priority. The proposed work can aid in providing a better understanding of the ease of reuse for software systems and can be used as a tool to assess its overall quality.